The present invention relates generally to artificial neuron apparatus, and more particularly to artificial neurons based on resistive memory cells.
Neuromorphic technology relates to computing systems which are inspired by biological architectures of the nervous system. The conventional computing paradigm based on CMOS logic and von Neumann architecture is becoming increasingly inadequate to meet the expanding processing demands placed on modern computer systems. Compared to biological systems, it is also highly inefficient in terms of power consumption and space requirements. For example, the IBM Watson supercomputer, which recently won the Jeopardy contest against two human contestants, has 2880 computing cores (the size of 10 refrigerators) and requires about 80 kW of power and 20 tonnes of air-conditioned cooling capacity. The human brain occupies less than 2 liters and consumes around 20 W of power. Simulating 5 seconds of brain activity using state-of-the-art supercomputers takes around 500 s and needs 1.4 MW of power. These issues have prompted a significant research effort to understand the highly efficient computational paradigm of the human brain and to create artificial cognitive systems with unprecedented computing power.
Neurons and synapses are two basic computational units in the brain. A neuron can integrate input signals coming from other neurons, in some cases with further inputs, for example from sensory receptors. At some point the neuron will “fire”, generating an output signal known as an “action potential” or “spike”, and then revert to its initial state. The spikes are conveyed to other neurons via synapses which change their connection strength as a result of neuronal activity. FIG. 1 of the accompanying drawings shows a schematic representation of two neurons N1, N2 interconnected via a synapse 1. Neuron N1 integrates inputs from other neurons, sensory receptors, etc. which it receives via dendrites 2. When neuron N1 fires, it generates a spike which is conveyed to other neurons via an axon 3 of N1. The synapse 1 interconnecting pre-synaptic neuron N1 and post-synaptic neuron N2 receives spikes generated by neuron N1 and provides output signals to neuron N2. The resulting synaptic output signal is conveyed to neuron N2 via a dendrite 4 of N2. This synaptic output signal is a graded synaptic potential which depends on conductance (also known as “synaptic weight” or “strength”) of the synapse. Synaptic weight can be enhanced or reduced by neuronal activity, and this “plasticity” of synapses is crucial to memory and other brain functions. This effect is indicated in FIG. 1 by back-propagation of a spike generated by N2 to the synapse 1 via a dendrite 5 of neuron N2.
Most current artificial neuron realizations are based on hybrid analog/digital VLSI circuits. These so-called silicon neurons require several transistors to be realized and are not particularly suitable for integration with emerging nanoscale devices such as resistive memory cells (memristors). Resistive memory cells such as phase change memory (PCM) cells have been recognized as suitable candidates for the realization of neural hardware (see e.g. “The Ovonic Cognitive Computer—A New Paradigm”, Ovshinsky, Proc. E/PCOS, 2004, and “Novel Applications Possibilities for Phase-Change Materials and Devices”, Wright et al., Proc. E/PCOS, 2013). Resistive memory cells are programmable-resistance devices which rely on the variable resistance characteristics of a volume of resistive material disposed between a pair of electrodes. Cell resistance can be programmed by application of control signals (“programming” or “write” signals) to the electrodes. These cells exhibit a threshold-switching effect whereby the cell can be switched between high and low resistance states by applying a control signal above a threshold level. By appropriate adjustment of the control signals, cells may be programmed to a range of intermediate resistance values, whereby application of successive signals can progressively modify cell-resistance. The cell-resistance can be measured (or “read”) by applying a low-voltage control signal to the electrodes and measuring the resulting current flow through the cell. The control signal level for the read operation is low enough that the read operation does not disturb the programmed cell-state.
Prior proposals for neuromorphic neurons based on resistive memory cells were only aimed at capturing some characteristics of biological neurons such as the integrate-and-fire functionality. There have also been attempts at using such memory elements to emulate the generation of action potentials in biological neurons. There has been no concrete proposal for realizing a ready-to-integrate artificial neuron for neuromorphic hardware that can capture most of the essential attributes of a biological neuron.